# US 10,769,518 B1 · Method of controlling for undesired factors in machine learning models > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,769,518 B1 - Document ID: US10769518B1 - Status: Granted - Kind: B1 - Application number: 15/383,499 - Filed: 2016-12-19 - Granted: 2020-09-08 - Earliest claimed: 2015-12-29 - Assignees: State Farm Mutual Automobile Insurance Company - Applicants: State Farm Mutual Automobile Insurance Company - Inventors: Jeffrey S. Myers; Kenneth J. Sanchez; Michael L. Bernico - Web page: https://patentgrep.com/patent/US10769518B1 ## CPC - G06Q 40/08: Insurance - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06V 10/82: using neural networks - G06V 30/19173: {Classification techniques} - G06V 40/169: {Holistic features and representations, i.e. based on the facial image taken as a whole} - H04N 7/185: {from a mobile camera, e.g. for remote control} - G06N 3/045: Combinations of networks - G06Q 30/0207: Discounts or incentives, e.g. coupons or rebates - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract A method of training and using a machine learning model that controls for consideration of undesired factors which might otherwise be considered by the trained model during its subsequent analyses of new data. For example, the model may be a neural network trained on a set of training images to evaluate an insurance applicant based upon an image or audio data of the insurance applicant as part of an underwriting process to determine an appropriate life or health insurance premium. The model is trained to probabilistically correlate an aspect of the applicant's appearance with a personal and/or health-related characteristic. Any undesired factors, such as age, sex, ethnicity, and/or race, are identified for exclusion. The trained model receives the image (e.g., a “selfie”) of the insurance applicant, analyzes the image without considering the identified undesired factors, and suggests the appropriate insurance premium based only on the remaining desired factors. ## Claims ### Claim 1 A computer-implemented method of training and using a machine learning model that controls for consideration of one or more undesired factors which might otherwise be considered by the machine learning model when analyzing new data, the method comprising, via one or more processors: training the machine learning model using a training data set that contains information including the one or more undesired factors; identifying the one or more undesired factors and one or more relevant interaction terms between the one or more undesired factors; and causing the trained machine learning model to not consider the identified one or more undesired factors when analyzing the new data to control for undesired prejudice or discrimination in machine learning models, wherein identifying the one or more undesired factors and the one or more relevant interaction terms between the one or more undesired factors includes training a second machine learning model using a second training data set that contains only the one or more undesired factors and one or more relevant interaction terms between the one or more undesired factors. ### Claim 2 (depends on 1) The computer-implemented method as set forth in claim 1, wherein causing the machine learning model to not consider the identified one or more undesired factors when analyzing the new data includes combining the machine learning model and the second machine learning model to eliminate a bias created by the one or more undesired factors from the machine learning model's consideration prior to employing the machine learning model to analyze the new data. ### Claim 3 (depends on 1) The computer-implemented method as set forth in claim 1, wherein identifying the one or more undesired factors and the one or more relevant interaction terms between the one or more undesired factors includes training the machine learning model to identify the one or more undesired factors and the one or more relevant interaction terms between the one or more undesired factors. ### Claim 4 (depends on 3) The computer-implemented method as set forth in claim 3, wherein causing the machine learning model to not consider the identified one or more undesired factors when analyzing the new data includes instructing the machine learning model to not consider the identified one or more undesired factors while analyzing the new data. ### Claim 5 (depends on 1) The computer-implemented method as set forth in claim 1, wherein the machine learning model is a neural network. ### Claim 6 (depends on 1) The computer-implemented method as set forth in claim 1, wherein the second machine learning model is a linear model. ### Claim 7 (depends on 1) The computer-implemented method as set forth in claim 1, wherein the machine learning model is trained to analyze the new data as part of an underwriting process to determine an appropriate insurance premium. ### Claim 8 (depends on 7) The computer-implemented method as set forth in claim 7, wherein the new data includes a still image or a video of a person applying for life insurance or health insurance. ### Claim 9 (depends on 7) The computer-implemented method as set forth in claim 7, wherein the new data includes an image of a piece of property for which a person is applying for property insurance. ### Claim 10 (depends on 7) The computer-implemented method as set forth in claim 7, wherein the machine learning model is further trained to analyze the new data as part of the underwriting process to determine one or more appropriate terms of coverage. ### Claim 11 A computer system configured to train and use a machine learning model that controls for consideration of one or more undesired factors which might otherwise be considered by the machine learning model when analyzing new data, the computer system comprising one or more processors configured to: train the machine learning model using a training data set that contains information including the one or more undesired factors; identify the one or more undesired factors and one or more relevant interaction terms between the one or more undesired factors; and cause the trained machine learning model to not consider the identified one or more undesired factors when analyzing the new data to control for undesired prejudice or discrimination in machine learning models, wherein identifying the one or more undesired factors and the one or more relevant interaction terms between the one or more undesired factors includes the one or more processors training a second machine learning model using a second training data set that contains only the one or more undesired factors and one or more relevant interaction terms between the one or more undesired factors. ### Claim 12 (depends on 11) The computer system as set forth in claim 11, wherein causing the machine learning model to not consider the identified one or more undesired factors when analyzing the new data includes the one or more processors combining the machine learning model and the second machine learning model to eliminate a bias created by the one or more undesired factors from the machine learning model's consideration prior to employing the machine learning model to analyze the new data. ### Claim 13 (depends on 11) The computer system as set forth in claim 11, wherein identifying the one or more undesired factors and the one or more relevant interaction terms between the one or more undesired factors includes the one or more processors training the machine learning model to identify the one or more undesired factors and the one or more relevant interaction terms between the one or more undesired factors. ### Claim 14 (depends on 11) The computer system as set forth in claim 11, wherein causing the machine learning model to not consider the identified one or more undesired factors when analyzing the new data includes the one or more processors instructing the machine learning model to not consider the identified one or more undesired factors while analyzing the new data. ### Claim 15 (depends on 11) The computer system as set forth in claim 11, wherein the machine learning model is a neural network. ### Claim 16 (depends on 11) The computer system as set forth in claim 11, wherein the second machine learning model is a linear model. ### Claim 17 (depends on 11) The computer system as set forth in claim 11, wherein the machine learning model is trained to analyze the new data as part of an underwriting process to determine an appropriate insurance premium. ### Claim 18 (depends on 11) The computer system as set forth in claim 11, wherein the new data includes images of a person applying for life insurance or health insurance.